Workflow n8n
Automatisation d'alertes d'erreurs avec n8n : notifications par email
Ce workflow n8n a pour objectif d'automatiser la gestion des erreurs en envoyant des alertes par email dès qu'une erreur est détectée. Dans un contexte professionnel où la réactivité est cruciale, ce type d'automatisation permet aux équipes techniques de réagir rapidement aux problèmes, minimisant ainsi les temps d'arrêt et améliorant la satisfaction client. Les cas d'usage incluent la surveillance des systèmes, la gestion des incidents et l'amélioration continue des processus opérationnels. Étape 1 : le workflow débute avec un déclencheur d'erreur qui active le processus dès qu'une erreur survient. Étape 2 : les détails de l'erreur sont extraits à l'aide du nœud 'Extract Error Details', qui collecte les informations nécessaires pour une analyse approfondie. Étape 3 : le modèle de chat OpenAI est utilisé pour générer une réponse ou une solution potentielle à l'erreur détectée. Étape 4 : les résultats sont ensuite formatés et préparés pour l'envoi par email grâce au nœud 'Generate Email'. Enfin, l'email est envoyé via Gmail, informant les parties concernées de l'incident et des actions recommandées. Cette automatisation n8n offre une valeur ajoutée significative en réduisant le temps de réponse aux incidents et en améliorant la communication au sein des équipes. Tags clés : automatisation, n8n, alertes.
Catégorie: Webhook · Tags: automatisation, n8n, alertes, email, gestion des erreurs❤ 0
Vue d'ensemble du workflow n8n
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Détail des nœuds du workflow n8n
Inscris-toi pour voir l'intégralité du workflow
Inscription gratuite
S'inscrire gratuitementBesoin d'aide ?{
"id": "3b1q6ZJTxeONrpUV",
"meta": {
"instanceId": ""
},
"name": "Error Alert and Summarizer",
"tags": [],
"nodes": [
{
"id": "d29a5b06-1609-416f-bc74-0274d3321019",
"name": "Error Trigger",
"type": "n8n-nodes-base.errorTrigger",
"position": [
-600,
-40
],
"parameters": {},
"typeVersion": 1
},
{
"id": "a71d3052-a89b-4e8e-baee-7fe245575f42",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
528,
180
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o",
"cachedResultName": "gpt-4o"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "786",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "e71dee7b-4dfd-49ab-8939-f3808ee112d7",
"name": "Structured Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
648,
180
],
"parameters": {
"jsonSchemaExample": "{\n\"diagnosis\":\"\",\n\"cause\":\"\",\n\"resolution\":\"\"\n}"
},
"typeVersion": 1.2
},
{
"id": "3611e9e8-f677-49c4-b06c-fa6c28f43930",
"name": "SET EMAIL",
"type": "n8n-nodes-base.set",
"position": [
-380,
-40
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "45e1443a-fb44-42f8-96ad-423197c7265b",
"name": "TO",
"type": "string",
"value": "myemail@myemail.com"
},
{
"id": "968b05dc-f476-4e13-8166-e62005d0f936",
"name": "CC",
"type": "string",
"value": "theiremail@theiremail.com"
},
{
"id": "570663c5-29c0-44fb-9992-908b7cca8136",
"name": "BCC",
"type": "string",
"value": "theiremail@theiremail.com"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "3676f72e-d06d-44f8-be35-19efe09a257e",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-450,
-260
],
"parameters": {
"color": 3,
"height": 380,
"content": "# SET YOUR EMAILS"
},
"typeVersion": 1
},
{
"id": "f0b08a20-6ecc-4487-9a0a-30be07cc0cbb",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-40,
-260
],
"parameters": {
"color": 3,
"width": 280,
"height": 380,
"content": "# Enable/Disable Manual Executions"
},
"typeVersion": 1
},
{
"id": "b35cd2a6-5f22-4e06-9bb0-880855c423a8",
"name": "Remove Manual Exec",
"type": "n8n-nodes-base.if",
"position": [
60,
-40
],
"parameters": {
"options": {
"ignoreCase": true
},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": false,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "9b2f3ff3-db9c-406b-a97f-37620dc5fab9",
"operator": {
"type": "string",
"operation": "notContains"
},
"leftValue": "={{ $json.mode }}",
"rightValue": "manual"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "2a33b02a-78f1-4243-ba7d-f217ea4d1895",
"name": "Get Failed Exec",
"type": "n8n-nodes-base.n8n",
"position": [
-160,
-40
],
"parameters": {
"options": {
"activeWorkflows": true
},
"resource": "execution",
"operation": "get",
"executionId": "={{ $('Error Trigger').item.json.execution.id }}",
"requestOptions": {}
},
"credentials": {
"n8nApi": {
"id": "786",
"name": "n8n account"
}
},
"typeVersion": 1
},
{
"id": "b36ccbf9-4e47-44fc-aed3-424b6f121329",
"name": "Extract Error Details",
"type": "n8n-nodes-base.code",
"position": [
280,
-40
],
"parameters": {
"jsCode": "// 1) Grab your full execution JSON\nconst exec = items[0].json;\n\n// 2) Build execution‐level metadata\nconst meta = {\n executionId: exec.id,\n finished: exec.finished,\n mode: exec.mode,\n status: exec.status,\n createdAt: exec.createdAt,\n startedAt: exec.startedAt,\n stoppedAt: exec.stoppedAt,\n deletedAt: exec.deletedAt,\n workflowId: exec.workflowId,\n workflowName: exec.workflowData?.name,\n retryOf: exec.retryOf,\n retrySuccessId: exec.retrySuccessId,\n};\n\n// 3) Identify trigger node name from startData\nconst runNodeFilter = exec.data?.startData?.runNodeFilter || [];\nconst triggerNodeName = runNodeFilter[0] || null;\n\n// 4) Grab the raw trigger runData\nconst runData = exec.data?.resultData?.runData || {};\nconst triggerRuns = triggerNodeName ? (runData[triggerNodeName] || []) : [];\n\n// 5) Extract the JSON payload from the first run of the trigger\nlet triggerPayload = {};\nif (triggerRuns.length && triggerRuns[0].data?.main?.[0]?.[0]?.json) {\n triggerPayload = triggerRuns[0].data.main[0][0].json;\n}\n\n// 6) Merge trigger info into meta\nmeta.triggerNodeName = triggerNodeName;\nmeta.triggerPayload = triggerPayload;\n\n// 7) Now scan for all node errors, **excluding** any nodeName that contains “SERP”\nconst allErrors = [];\nfor (const [nodeName, runs] of Object.entries(runData)) {\n // Skip any of the SERP nodes\n if (nodeName.includes('SERP')) continue;\n\n runs.forEach(run => {\n if (run.executionStatus === 'error') {\n const err = run.error || exec.data.resultData.error || {};\n const nodeDef = err.node || run.node || {};\n\n allErrors.push({\n ...meta, // exec + trigger metadata\n\n nodeName,\n nodeId: nodeDef.id,\n nodeType: nodeDef.type,\n nodeLabel: nodeDef.name,\n\n startTime: run.startTime,\n executionTime: run.executionTime,\n source: run.source,\n\n errorName: err.name,\n errorMessage: err.message,\n errorDescription: err.description,\n httpCode: err.httpCode,\n messages: err.messages,\n context: err.context,\n stack: err.stack,\n\n parameters: nodeDef.parameters,\n credentials: nodeDef.credentials,\n });\n }\n });\n}\n\n// 8) Return results\nif (!allErrors.length) {\n return [{ json: { message: '✅ No (non‑SERP) errors found in this execution.' } }];\n}\nreturn allErrors.map(e => ({ json: e }));\n"
},
"typeVersion": 2
},
{
"id": "a26fb0c8-99eb-466d-b201-89c402fa1af4",
"name": "Error Solver Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
500,
-40
],
"parameters": {
"text": "=Can you please help me with this error that occured in my n8n workflow? {{ JSON.stringify($json) }}",
"options": {
"systemMessage": "You are an seasoned n8n expert with specializations in managing n8n instances and workflows. The user will provide a detailed error json object and your goal is to review, analyze and understand the error and using your expertise diagnose the error and provide a detailed report to the user with your diagnosis, cause and resolution so the user understands and can immediately fix the issue."
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.8
},
{
"id": "8cfd7229-3ff1-4ba1-a67d-caa21be8064f",
"name": "Set Diagnosis Fields",
"type": "n8n-nodes-base.set",
"position": [
876,
-40
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "fac5fbee-d63d-4148-b047-5ed5af4f2574",
"name": "error.diagnosis",
"type": "string",
"value": "={{ $json.output.diagnosis }}"
},
{
"id": "ece9388d-f667-4984-a143-7241f622fe76",
"name": "error.cause",
"type": "string",
"value": "={{ $json.output.cause }}"
},
{
"id": "acb6b34a-a651-42fc-a44a-331b2e0d745c",
"name": "error.resolution",
"type": "string",
"value": "={{ $json.output.resolution }}"
},
{
"id": "c765754b-d6d5-4592-ac3f-99a350bc3c19",
"name": "error.workflowName",
"type": "string",
"value": "={{ $('Extract Error Details').item.json.workflowName }}"
},
{
"id": "dabebc62-3e0c-4d22-afbf-54ba66a912fb",
"name": "error.workflowId",
"type": "string",
"value": "={{ $('Extract Error Details').item.json.workflowId }}"
},
{
"id": "6ab19800-9a0f-439f-bf62-7a7afc5bf958",
"name": "workflowLink",
"type": "string",
"value": "={{ $execution.resumeUrl.split('/').slice(0, 3).join('/') }}/workflow/{{ $('Extract Error Details').item.json.workflowId }}"
},
{
"id": "29daaea5-052b-46d4-8192-141db159bff2",
"name": "error.executionId",
"type": "string",
"value": "={{ $('Extract Error Details').item.json.executionId }}"
},
{
"id": "9e4e553c-c82b-41ec-8ee2-14162cdc3bd8",
"name": "executionLink",
"type": "string",
"value": "={{ $execution.resumeUrl.split('/').slice(0, 3).join('/') }}/workflow/{{ $('Extract Error Details').item.json.workflowId }}/executions/{{ $('Extract Error Details').item.json.executionId }}"
},
{
"id": "7269ea9f-ed49-46cd-89f2-d4a467da529d",
"name": "error.finished",
"type": "boolean",
"value": "={{ $('Extract Error Details').item.json.finished }}"
},
{
"id": "29a6e6d2-5058-4dd9-b2f9-3980a6a9073a",
"name": "error.startedAt",
"type": "string",
"value": "={{ $('Extract Error Details').item.json.startedAt }}"
},
{
"id": "a0ad0e13-5a6e-48db-9a80-74c09434de7f",
"name": "error.nodeName",
"type": "string",
"value": "={{ $('Extract Error Details').item.json.nodeName }}"
},
{
"id": "6c1001d4-a581-4520-9f16-a2c7cf0e1f84",
"name": "error.previousNode",
"type": "string",
"value": "={{ $('Extract Error Details').item.json.source[0].previousNode }}"
},
{
"id": "8c3402ca-3f15-44ae-9b96-ea37c174334c",
"name": "rawJson",
"type": "string",
"value": "={{ JSON.stringify($('Extract Error Details').item.json) }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "9e95edf0-b2f1-443b-9ac4-3e3b3311cad5",
"name": "Send Gmail",
"type": "n8n-nodes-base.gmail",
"position": [
1316,
-40
],
"webhookId": "2f253c1f-36c3-4d58-ba2f-3a50bb78f188",
"parameters": {
"sendTo": "={{ $('SET EMAIL').item.json.TO }}",
"message": "={{ $json.html }}",
"options": {
"ccList": "={{ $('SET EMAIL').item.json.CC }}",
"bccList": "={{ $('SET EMAIL').item.json.BCC }}",
"appendAttribution": true
},
"subject": "={{ $json.subject }}"
},
"credentials": {
"gmailOAuth2": {
"id": "786",
"name": "Gmail account"
}
},
"typeVersion": 2.1
},
{
"id": "1705ee42-0be4-41a2-8ff9-f6963eef7382",
"name": "Generate Email",
"type": "n8n-nodes-base.code",
"position": [
1100,
-40
],
"parameters": {
"jsCode": "// 1. Pull in your error payload\nconst rawInput = items[0].json;\nconst parsed = typeof rawInput === 'string' ? JSON.parse(rawInput) : rawInput;\nconst errorArray = Array.isArray(parsed) ? parsed : [parsed];\n\n// 2. Build HTML & Markdown sections\nlet htmlSections = '';\n\n\nfor (const errObj of errorArray) {\n const {\n error: {\n workflowName,\n executionId,\n nodeName,\n previousNode,\n diagnosis,\n cause,\n resolution,\n startedAt,\n },\n workflowLink,\n executionLink,\n } = errObj;\n\n // HTML block\n htmlSections += `\n <div style=\"border:1px solid #ddd;border-radius:4px;padding:16px;margin-bottom:20px;background:#fafafa;\">\n <h3 style=\"margin:0 0 10px;color:#c0392b;font-family:Arial,sans-serif;\">\n 🛑 ${workflowName} — Error in node: ${nodeName}\n </h3>\n <p style=\"margin:4px 0;font-family:Arial,sans-serif;\">\n <strong>Workflow:</strong> \n <a href=\"${workflowLink}\" style=\"color:#2980b9;text-decoration:none;\">\n ${workflowName}\n </a><br/>\n <strong>Execution:</strong> \n <a href=\"${executionLink}\" style=\"color:#2980b9;text-decoration:none;\">\n #${executionId}\n </a><br/>\n <strong>Previous Node:</strong> ${previousNode}<br/>\n <strong>Started At:</strong> ${new Date(startedAt).toLocaleString('en-US', { timeZone: 'America/New_York' })}\n </p>\n <hr style=\"border:none;border-top:1px solid #ccc;margin:12px 0;\"/>\n <h4 style=\"margin:0 0 6px;color:#e67e22;font-family:Arial,sans-serif;\">🔍 Diagnosis</h4>\n <p style=\"margin:4px 0 12px;font-family:Arial,sans-serif;\">${diagnosis}</p>\n <h4 style=\"margin:0 0 6px;color:#e67e22;font-family:Arial,sans-serif;\">⚙️ Cause</h4>\n <p style=\"margin:4px 0 12px;font-family:Arial,sans-serif;\">${cause}</p>\n <h4 style=\"margin:0 0 6px;color:#e67e22;font-family:Arial,sans-serif;\">✅ Resolution</h4>\n <p style=\"white-space:pre-wrap;margin:4px 0;font-family:Arial,sans-serif;\">${resolution}</p>\n </div>`;\n\n// 3. Wrap up\nconst html = `\n <div style=\"font-family:Arial,sans-serif;color:#333;background:#fff;padding:20px;\">\n <h2 style=\"margin-top:0;color:#2c3e50;\">Automated Error Report</h2>\n ${htmlSections}\n <p style=\"font-size:12px;color:#777;font-family:Arial,sans-serif;\">\n This message was generated automatically by \n <a href=\"https://realsimple.dev\" style=\"color:#777;text-decoration:none;\"><b>Real Simple Solutions</b></a>.\n</p>\n<div style=\"background:#f0f4ff;padding:8px 12px;margin-top:6px;border-radius:6px;font-size:12px;font-family:Arial,sans-serif;\">\n ✨ <strong>Want more n8n AI automation templates?</strong><br>\n Check out our full collection on \n <a href=\"https://joeper.es/4jXyRub\" style=\"color:#0066cc;text-decoration:none;\"><b>Gumroad</b></a>.\n</div>\n </div>\n`;\n\n// 4. Return all three\nreturn [\n {\n json: {\n subject: `🚨 n8n Error: ${errorArray[0].error.workflowName} (#${errorArray[0].error.executionId})`,\n html\n },\n },\n];\n"
},
"typeVersion": 2
}
],
"active": true,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "be484a20-26cd-4df4-a993-f7d01c2956e6",
"connections": {
"SET EMAIL": {
"main": [
[
{
"node": "Get Failed Exec",
"type": "main",
"index": 0
}
]
]
},
"Error Trigger": {
"main": [
[
{
"node": "SET EMAIL",
"type": "main",
"index": 0
}
]
]
},
"Generate Email": {
"main": [
[
{
"node": "Send Gmail",
"type": "main",
"index": 0
}
]
]
},
"Get Failed Exec": {
"main": [
[
{
"node": "Remove Manual Exec",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Error Solver Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Error Solver Agent": {
"main": [
[
{
"node": "Set Diagnosis Fields",
"type": "main",
"index": 0
}
]
]
},
"Remove Manual Exec": {
"main": [
[
{
"node": "Extract Error Details",
"type": "main",
"index": 0
}
]
]
},
"Set Diagnosis Fields": {
"main": [
[
{
"node": "Generate Email",
"type": "main",
"index": 0
}
]
]
},
"Extract Error Details": {
"main": [
[
{
"node": "Error Solver Agent",
"type": "main",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "Error Solver Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
}
}
}Pour qui est ce workflow ?
Ce workflow s'adresse principalement aux équipes techniques et aux responsables IT dans les entreprises de taille moyenne à grande, cherchant à améliorer leur réactivité face aux incidents. Un niveau technique intermédiaire est recommandé pour la mise en place et la personnalisation de ce workflow.
Problème résolu
Ce workflow résout le problème de la gestion des erreurs en automatisant le processus d'alerte. Avant sa mise en place, les équipes pouvaient perdre un temps précieux à détecter et à signaler manuellement les erreurs, ce qui entraînait des retards dans la résolution des problèmes. Grâce à cette automatisation, les utilisateurs reçoivent des notifications instantanées par email, ce qui leur permet d'agir rapidement et de réduire les impacts négatifs sur les opérations.
Étapes du workflow
Étape 1 : le workflow démarre avec le nœud 'Error Trigger', qui détecte toute erreur survenant dans le système. Étape 2 : une fois l'erreur détectée, le nœud 'Get Failed Exec' est utilisé pour récupérer les détails de l'exécution échouée. Étape 3 : les informations sont ensuite traitées par le nœud 'Extract Error Details', qui extrait les données pertinentes. Étape 4 : le modèle OpenAI est appelé via le nœud 'OpenAI Chat Model' pour générer une réponse ou une solution. Étape 5 : les résultats sont formatés dans un email grâce au nœud 'Generate Email', et enfin, l'email est envoyé via le nœud 'Send Gmail'.
Guide de personnalisation du workflow n8n
Pour personnaliser ce workflow, vous pouvez modifier le nœud 'SET EMAIL' pour changer l'adresse email de destination ou le contenu du message. Il est également possible d'ajuster les paramètres du modèle OpenAI dans le nœud 'OpenAI Chat Model' pour adapter les réponses générées. Si vous souhaitez intégrer d'autres outils, envisagez d'ajouter des nœuds supplémentaires pour la gestion des tickets ou des notifications sur des plateformes comme Slack ou Discord. Assurez-vous de tester le workflow après chaque modification pour garantir son bon fonctionnement.